#sparse-matrices2 articles

2 articles

AIBullisharXiv – CS AI · Feb 276/106

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Large Language Model Compression with Global Rank and Sparsity Optimization

Researchers propose a novel two-stage compression method for Large Language Models that uses global rank and sparsity optimization to significantly reduce model size. The approach combines low-rank and sparse matrix decomposition with probabilistic global allocation to automatically detect redundancy across different layers and manage component interactions.

AIBullishHugging Face Blog · Sep 106/105

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Block Sparse Matrices for Smaller and Faster Language Models

The article discusses block sparse matrices as a technique to create smaller and faster language models. This approach could significantly reduce computational requirements and memory usage in AI systems while maintaining performance.